At the same time, progress in distributed systems and algorithms has been fundamental to the recent success of deep learning. Various advancements in developing distributed variants of gradient descent algorithms, and deep learning frameworks [1]}, [2]}, [3]} have been invented that scale-up training required for growth in the field. Thus, researchers proposed and developed distributed architectures for DRL empowering agents to learn from experience faster, leverage exploration strategies, and become capable of learning a diverse set of tasks simultaneously.
